{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LDA算法简介和应用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算法简介"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 线性判别模型（LDA）在模式识别领域（比如人脸识别等图形图像识别领域）中有非常广泛的应用。LDA是一种监督学习的降维技术，也就是说它的数据集的每个样本是有类别输出的。这点和PCA不同。PCA是不考虑样本类别输出的无监督降维技术。LDA的思想可以用一句话概括，就是“投影后类内方差最小，类间方差最大”。我们要将数据在低维度上进行投影，投影后希望每一种类别数据的投影点尽可能的接近，而不同类别的数据的类别中心之间的距离尽可能的大。即：将数据投影到维度更低的空间中，使得投影后的点，会形成按类别区分，一簇一簇的情况，相同类别的点，将会在投影后的空间中更接近方法。\n",
    "\n",
    "# LDA算法的主要优点：\n",
    "#     1.在降维过程中可以使用类别的先验知识经验，而像PCA这样的无监督学习则无法使用类别先验知识；\n",
    "#     2.LDA在样本分类信息依赖均值而不是方差的时候，比PCA之类的算法较优。\n",
    "\n",
    "# LDA算法的主要缺点：\n",
    "#     1.LDA不适合对非高斯分布样本进行降维，PCA也有这个问题\n",
    "#     2.LDA降维最多降到类别数 k-1 的维数，如果我们降维的维度大于 k-1，则不能使用 LDA。当然目前有一些LDA的进化版算法可以绕过这个问题\n",
    "#     3.LDA在样本分类信息依赖方差而不是均值的时候，降维效果不好\n",
    "#     4.LDA可能过度拟合数据,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算法应用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LDA在模式识别领域（比如人脸识别，舰艇识别等图形图像识别领域）中有非常广泛的应用，因此我们有必要了解一下它的算法原理。不过在学习LDA之前，我们有必要将其与自然语言处理领域中的LDA区分开，在自然语言处理领域，LDA是隐含狄利克雷分布（Latent DIrichlet Allocation，简称LDA），它是一种处理文档的主题模型，我们本文讨论的是线性判别分析，因此后面所说的LDA均为线性判别分析。\n",
    "\n",
    "# LDA除了可以用于降维以外，还可以用于分类。一个常见的LDA分类基本思想是假设各个类别的样本数据符合高斯分布，这样利用LDA进行投影后，可以利用极大似然估计计算各个类别投影数据的均值和方差，进而得到该类别高斯分布的概率密度函数。当一个新的样本到来后，我们可以将它投影，然后将投影后的样本特征分别带入各个类别的高斯分布概率密度函数，计算它属于这个类别的概率，最大的概率对应的类别即为预测类别。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Demo实践"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 库函数导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis # 导入LDA类\n",
    "from sklearn.datasets ._samples_generator import make_classification # 导入生成样本数据集的函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 制作四个类别的数据，每个类别100个样本\n",
    "X, y = make_classification(n_samples=1000, n_features=3, n_redundant=0,\n",
    "                           n_classes=4, n_informative=2, n_clusters_per_class=1,\n",
    "                           class_sep=3, random_state=10)\n",
    "# 将四个类别的数据进行三维显示\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig, rect=[0, 0, 1, 1], elev=20, azim=20)\n",
    "fig.add_axes(ax) # 这一句不能忘\n",
    "ax.scatter(X[:, 0], X[:, 1], X[:, 2], marker='o', c=y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearDiscriminantAnalysis()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearDiscriminantAnalysis</label><div class=\"sk-toggleable__content\"><pre>LinearDiscriminantAnalysis()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearDiscriminantAnalysis()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 建立LDA模型\n",
    "lda = LinearDiscriminantAnalysis()\n",
    "# 训练模型\n",
    "lda.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'covariance_estimator': None,\n",
       " 'n_components': None,\n",
       " 'priors': None,\n",
       " 'shrinkage': None,\n",
       " 'solver': 'svd',\n",
       " 'store_covariance': False,\n",
       " 'tol': 0.0001}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看LDA参数\n",
    "lda.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. 数据模型可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 进行模型预测\n",
    "X_new = lda.transform(X)\n",
    "# 可视化预测数据\n",
    "plt.scatter(X_new[:, 0], X_new[:, 1], marker='o', c=y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.   0.1  0.1]]类别是： [0]\n",
      "[[-1.   0.1  0.1]]类别概率分别是: [[9.37611354e-01 1.88760664e-05 3.36891510e-02 2.86806189e-02]]\n",
      "[[ -12 -100  -91]]类别是： [1]\n",
      "[[ -12 -100  -91]]类别概率分别是: [[1.08769337e-028 1.00000000e+000 1.54515810e-221 9.05666876e-183]]\n",
      "[[-12.   -0.1  -0.1]]类别是： [2]\n",
      "[[-12.   -0.1  -0.1]]类别概率分别是: [[1.60268201e-07 1.46912978e-39 9.99999840e-01 3.57001075e-28]]\n",
      "[[ 0.1 90.1  9.1]]类别是： [3]\n",
      "[[ 0.1 90.1  9.1]]类别概率分别是: [[8.42065614e-08 9.45021749e-11 8.63060269e-02 9.13693889e-01]]\n"
     ]
    }
   ],
   "source": [
    "# 进行新的测试数据测试\n",
    "a = np.array([[-1,0.1,0.1]])\n",
    "print(f\"{a}类别是：\",lda.predict(a))\n",
    "print(f\"{a}类别概率分别是:\", lda.predict_proba(a))\n",
    "\n",
    "a = np.array([[-12, -100, -91]])\n",
    "print(f\"{a}类别是：\",lda.predict(a))\n",
    "print(f\"{a}类别概率分别是:\", lda.predict_proba(a))\n",
    "\n",
    "a = np.array([[-12, -0.1, -0.1]])\n",
    "print(f\"{a}类别是：\",lda.predict(a))\n",
    "print(f\"{a}类别概率分别是:\", lda.predict_proba(a))\n",
    "\n",
    "a = np.array([[0.1, 90.1, 9.1]])\n",
    "print(f\"{a}类别是：\",lda.predict(a))\n",
    "print(f\"{a}类别概率分别是:\", lda.predict_proba(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实践部分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于LDA手写数字分类实践"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis \n",
    "from sklearn.metrics import classification_report, confusion_matrix \n",
    "import matplotlib\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mnist dataset:\n",
      " {'data': array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],\n",
      "       [ 0.,  0.,  0., ..., 10.,  0.,  0.],\n",
      "       [ 0.,  0.,  0., ..., 16.,  9.,  0.],\n",
      "       ...,\n",
      "       [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
      "       [ 0.,  0.,  2., ..., 12.,  0.,  0.],\n",
      "       [ 0.,  0., 10., ..., 12.,  1.,  0.]]), 'target': array([0, 1, 2, ..., 8, 9, 8]), 'frame': None, 'feature_names': ['pixel_0_0', 'pixel_0_1', 'pixel_0_2', 'pixel_0_3', 'pixel_0_4', 'pixel_0_5', 'pixel_0_6', 'pixel_0_7', 'pixel_1_0', 'pixel_1_1', 'pixel_1_2', 'pixel_1_3', 'pixel_1_4', 'pixel_1_5', 'pixel_1_6', 'pixel_1_7', 'pixel_2_0', 'pixel_2_1', 'pixel_2_2', 'pixel_2_3', 'pixel_2_4', 'pixel_2_5', 'pixel_2_6', 'pixel_2_7', 'pixel_3_0', 'pixel_3_1', 'pixel_3_2', 'pixel_3_3', 'pixel_3_4', 'pixel_3_5', 'pixel_3_6', 'pixel_3_7', 'pixel_4_0', 'pixel_4_1', 'pixel_4_2', 'pixel_4_3', 'pixel_4_4', 'pixel_4_5', 'pixel_4_6', 'pixel_4_7', 'pixel_5_0', 'pixel_5_1', 'pixel_5_2', 'pixel_5_3', 'pixel_5_4', 'pixel_5_5', 'pixel_5_6', 'pixel_5_7', 'pixel_6_0', 'pixel_6_1', 'pixel_6_2', 'pixel_6_3', 'pixel_6_4', 'pixel_6_5', 'pixel_6_6', 'pixel_6_7', 'pixel_7_0', 'pixel_7_1', 'pixel_7_2', 'pixel_7_3', 'pixel_7_4', 'pixel_7_5', 'pixel_7_6', 'pixel_7_7'], 'target_names': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), 'images': array([[[ 0.,  0.,  5., ...,  1.,  0.,  0.],\n",
      "        [ 0.,  0., 13., ..., 15.,  5.,  0.],\n",
      "        [ 0.,  3., 15., ..., 11.,  8.,  0.],\n",
      "        ...,\n",
      "        [ 0.,  4., 11., ..., 12.,  7.,  0.],\n",
      "        [ 0.,  2., 14., ..., 12.,  0.,  0.],\n",
      "        [ 0.,  0.,  6., ...,  0.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  0., ...,  5.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ...,  9.,  0.,  0.],\n",
      "        [ 0.,  0.,  3., ...,  6.,  0.,  0.],\n",
      "        ...,\n",
      "        [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
      "        [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
      "        [ 0.,  0.,  0., ..., 10.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  0., ..., 12.,  0.,  0.],\n",
      "        [ 0.,  0.,  3., ..., 14.,  0.,  0.],\n",
      "        [ 0.,  0.,  8., ..., 16.,  0.,  0.],\n",
      "        ...,\n",
      "        [ 0.,  9., 16., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  3., 13., ..., 11.,  5.,  0.],\n",
      "        [ 0.,  0.,  0., ..., 16.,  9.,  0.]],\n",
      "\n",
      "       ...,\n",
      "\n",
      "       [[ 0.,  0.,  1., ...,  1.,  0.,  0.],\n",
      "        [ 0.,  0., 13., ...,  2.,  1.,  0.],\n",
      "        [ 0.,  0., 16., ..., 16.,  5.,  0.],\n",
      "        ...,\n",
      "        [ 0.,  0., 16., ..., 15.,  0.,  0.],\n",
      "        [ 0.,  0., 15., ..., 16.,  0.,  0.],\n",
      "        [ 0.,  0.,  2., ...,  6.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0.,  2., ...,  0.,  0.,  0.],\n",
      "        [ 0.,  0., 14., ..., 15.,  1.,  0.],\n",
      "        [ 0.,  4., 16., ..., 16.,  7.,  0.],\n",
      "        ...,\n",
      "        [ 0.,  0.,  0., ..., 16.,  2.,  0.],\n",
      "        [ 0.,  0.,  4., ..., 16.,  2.,  0.],\n",
      "        [ 0.,  0.,  5., ..., 12.,  0.,  0.]],\n",
      "\n",
      "       [[ 0.,  0., 10., ...,  1.,  0.,  0.],\n",
      "        [ 0.,  2., 16., ...,  1.,  0.,  0.],\n",
      "        [ 0.,  0., 15., ..., 15.,  0.,  0.],\n",
      "        ...,\n",
      "        [ 0.,  4., 16., ..., 16.,  6.,  0.],\n",
      "        [ 0.,  8., 16., ..., 16.,  8.,  0.],\n",
      "        [ 0.,  1.,  8., ..., 12.,  1.,  0.]]]), 'DESCR': \".. _digits_dataset:\\n\\nOptical recognition of handwritten digits dataset\\n--------------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 1797\\n    :Number of Attributes: 64\\n    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\\n    :Missing Attribute Values: None\\n    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\\n    :Date: July; 1998\\n\\nThis is a copy of the test set of the UCI ML hand-written digits datasets\\nhttps://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\\n\\nThe data set contains images of hand-written digits: 10 classes where\\neach class refers to a digit.\\n\\nPreprocessing programs made available by NIST were used to extract\\nnormalized bitmaps of handwritten digits from a preprinted form. From a\\ntotal of 43 people, 30 contributed to the training set and different 13\\nto the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\\n4x4 and the number of on pixels are counted in each block. This generates\\nan input matrix of 8x8 where each element is an integer in the range\\n0..16. This reduces dimensionality and gives invariance to small\\ndistortions.\\n\\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\\nT. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\\nL. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\\n1994.\\n\\n.. topic:: References\\n\\n  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\\n    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\\n    Graduate Studies in Science and Engineering, Bogazici University.\\n  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\\n  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\\n    Linear dimensionalityreduction using relevance weighted LDA. School of\\n    Electrical and Electronic Engineering Nanyang Technological University.\\n    2005.\\n  - Claudio Gentile. A New Approximate Maximal Margin Classification\\n    Algorithm. NIPS. 2000.\\n\"}\n"
     ]
    }
   ],
   "source": [
    "mnist = load_digits() \n",
    "\n",
    "print(\"The Mnist dataset:\\n\",mnist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, test_x, y_train, test_y = train_test_split(mnist.data, mnist.target, test_size=0.1, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[8], line 6\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[0;32m      4\u001b[0m images \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m9\u001b[39m)\n\u001b[1;32m----> 6\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mfigure(dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m)\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m images:\n\u001b[0;32m      9\u001b[0m     plt\u001b[38;5;241m.\u001b[39msubplot(\u001b[38;5;241m330\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m+\u001b[39mi)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "\n",
    "images = range(0, 9)\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "\n",
    "for i in images:\n",
    "    plt.subplot(330+1+i)\n",
    "    plt.imshow(x_train[i].reshape(8,8), cmap=matplotlib, interpolation=\"nearest\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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